Research Organization of Information and Systems

The Institute of
Statistical Mathematics
2017-2018

C O N T E N TS
■ Message from Director-General ——

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NOE ( Network Of Excellence ) Project
Project for Fostering and Promoting
Statistical Thinking
Producing Data Scientists in the Big Data Era
Cooperation with Mathematics Program

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■ History——Message from Director-General

The information criterion known as Akaike Information Criterion (AIC) — proposed by the late Dr. Hirotugu Akaike, the 8th Director-General of The Institute of
Statistical Mathematics, for the purpose of comparing statistical models — not
only had a major impact on the field of statistics itself but also succeeded in establishing a certain style of research, aspiring to achieve intellectual gains throughperpetual improvement of models, within a wide range of research disciplines.
The 1974 paper, published in IEEE Australia Council (IEEE-AC), that introduced
the philosophy of AIC provided a clear demonstration of its efficacy to the entire
community, and to this day, over 40 years after its publication, the number of citations of this paper continues to grow each year. This shows that the full magnitude of achievements in statistical mathematics research can only be assessedwith the benefit of many years (about 20 to 30 years) of hindsight, and that the
influence of those achievements remains relevant long into the future. Indeed, citations to the paper in Web of Science (WoS) and Scopus, the two primary databases for journal papers, peaked in 2015. Perhaps this is because one typical way
to use AIC is in selecting (explanatory) variables, and this has begun to be replaced by the modern techniques of sparse modeling and deep learning. Indeed,
today, nearly half a century after the birth of AIC, I have a feeling that a new era has come to statistical mathematics.
It is at this turning point that I assume my final term as Director-General (a two-year term spanning FY2017 and FY2018).
During my final term, I hope to strengthen three areas in particular: our research capabilities, our professional development facilities, and our collaboration with society. With operating support funds steadily shrinking year by year, and withincreasing priority placed on developing and expanding the Corporation’s headquarters operations, it has become difficult
to establish independent employment plans for our Institute itself. However, the imperatives of bringing together outstanding personnel to lead the research community, while simultaneously producing a steady stream of personnel capable ofshouldering the challenges of the future, remains the most critical mission of us, as an Inter-University Research Institute.
Thus, we will proceed with aggressive personnel recruiting; moreover, by reforming our core research organizations, wewill establish a new organizational structure that abounds in the diversity and flexibility required to meet the needs of future generations. Indeed, the reform trajectory on which we have embarked has already had noticeable consequences: thisfiscal year, we have employed five young assistant professors, and the share of full-time instructional positions occupied
by women has grown from 4% to 18%, a significant improvement, during my tenure as Director-General.
Improving our professional development facilities is among the highest priority obligations that we owe to society atlarge. Within this fiscal year, the six national universities will each establish a center to promote education throughout theuniversity basing the particular center related to mathematics and data science. In Japan, for many years it was the casethat, aside from our Institute, no organization existed for the systematic study of statistics and other data sciences; we are
extremely gratified that centers will be established to advance this important area of professional development. Whereas
the six university centers address training at all levels, from intern-level trainees to independent practitioners, our Institutewill develop professionals with the much advanced skills needed to lead their own project teams. As part of this initiative,at the end of the previous fiscal year, we signed the contract with the Center for Advanced Intelligence Project (AIP), RIKEN
including offering rapid-study courses and developing educational materials for machine learning. In the area of industrial
cooperation, we have continued to devote energy to several initiatives; including improving the Research Collaboration
Start-up as a single unified consultation portal and setting up the Data Science Research Plaza, at which employed researchers belonging to private industrial companies can sit down to learn or conduct research. We hope to develop our cooperation with private sectors in ways that remain clearly visible to the outside world, including offering courses linking institutions at the organizational level and calling for relatively large-scale collaborative research projects with the private sector.
It was in April 2011, just after the Great East Japan Earthquake, that the Director-General’s baton was passed on to me
from former Director-General Dr. Genshiro Kitagawa. In a time of great societal chaos and unrest, the fact that I, despite my
inexperience, managed somehow to withstand the pressure of great expectations and respond to the needs of this position is due entirely to the advice and guidance that I received from my wise mentors, the strong support of the entire staff,
and the assistance of the broader research communities. Going forward, I am committed to making the Institute an evenbetter place, and one that makes even more valuable contributions to society, and I look forward to receiving your advice
and guidance in this process.

Tomoyuki Higuchi
Director-General
The Institute of Statistical Mathematics1Institute Overview

Basic Research
Department of Statistical Modeling
The Department of Statistical Modeling works on the structural modeling of physical phenomenon related tonumerous factors, and it conducts research on model-based statistical inference methodologies. By means of themodeling of spatially and/or temporally varying phenomena, complex systems, and latent structures, the department
aims to contribute to the development of cross-field modeling intelligence.

■ Spatial and Time Series Modeling Group —————————————————————————————————————The Spatial and Time Series Modeling Group works on the development and evaluation of statistical models, which function effectively interms of predicting phenomena or scientific discoveries, through data analysis and modeling related to space-time-varying phenomena.

■ Complex System Modeling Group —————————————————————————————————————————The Complex System Modeling Group conducts studies in order to discover the structures of complex systems, such as
nonlinear systems and hierarchical networks, through statistical modeling.

■ Latent Structure Modeling Group ——————————————————————————————————————————The Latent Structure Modeling Group works on the modeling of variable factors as latent structures existing behindvarious dynamic phenomena in the real world. It also conducts research on methodologies for inference computation
associated with structures on the basis of data related to phenomena.

Department of Data Science
The aim of the Department of Data Science is to contribute to the development of natural and social sciences byconducting research into the methodology of designing statistical data collection systems, measuring and analyzing
complex phenomena for evidence-based sciences, and performing exploratory multivariate data analyses.

■ Survey Science Group ————————————————————————————————————————————————The Survey Science Group focuses on research toward designing statistical data collection systems and developing therelated data analysis methods in a variety of survey and experimental environments.

■ Metric Science Group —————————————————————————————————————————————————The Metric Science Group studies methods for measuring and analyzing complex phenomena to extract statisticalevidence behind them in the various fields of science.

■ Structure Exploration Group —————————————————————————————————————————————The Structure Exploration Group advances statistical and mathematical research by applying or developing exploratory
multivariate data analyses to clarify latent structures of real phenomena in various fields of both natural and social sciences.

Department of Mathematical Analysis and Statistical Inference
The Department of Mathematical Analysis and Statistical Inference carries out research into general statisticaltheory, statistical learning theory, optimization, and algorithms for statistical inference.

■ Mathematical Statistics Group ———————————————————————————————————————————The Mathematical Statistics Group is concerned with aspects of statistical inference theory, modeling of uncertain phenomena,
stochastic processes and their application to inference, probability and distribution theory, and the related mathematics.

■ Learning and Inference Group ————————————————————————————————————————————The Learning and Inference Group develops statistical methodologies to describe the stochastic structure of data
mathematically and clarify the potential and the limitations of the data theoretically.

■ Computational Inference Group ———————————————————————————————————————————The Computational Inference Group studies mathematical methodologies in the research fields of numerical analysis, optimization,
discrete mathematics, and control and systems theory for computation-based statistical inference, as well as their applications.

NOE-type Research
Risk Analysis Research Center
The Risk Analysis Research Center is pursuing a scientific approach to uncertainties and risks in society, which haveincreased with the growing globalization of society and economy. Our research projects are mainly seismology, finance,
resources, environmentology, medical-health science, database development and risk mathematics. The Center also
manages a network organization for risk analysis with the goal of contributing to creating a safe and resilient society.
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NOE-type Research
Research and Development Center for Data Assimilation
Research and Development Center for Data Assimilation aims to construct simulation models that can predict
the future and to design effective observation systems by means of “data assimilation”, which is a fundamental
technology integrating numerical simulations and observational data.

Research Center for Statistical Machine Learning
Machine learning is a research field associated with autonomous systems that can learn their behavior from data.
This field is based on both the statistical science concerning inference from data and computer science concerning
efficient algorithms. It can be applied to broad disciplines ranging engineering and information science to naturalscience, such as robotics and brain sciences. Our research center aims at supporting the academic community ofthis field, as well as producing influential research through various joint projects.

Data Science Center for Creative Design and Manufacturing
We aim to motivate advanced technologies in the field of data science, and foster scientific methodologies forcreative design and manufacturing. Population reduction and globalization are bringing dramatic change in theindustrial structure, resulting in a rapid lost of our global predominance in the manufacturing industry. Furthermore,countries around the world has actively launched new data science programs for the next generation design andmanufacturing. Following the global trend is no longer an effective way to survive in the intensive power gamearound the world. We have to develop innovative thinking, for which data science can provide the essential tools. We
have accumulated state-of-the-art data science technologies here, for instance, machine learning, optimization
theory, Bayesian inference, materials informatics, etc. We are devoted to foster innovative methods for design andmanufacturing through industry-academia collaboration. This is the mission of this center.

Professional Development
School of Statistical Thinking
The mission of the School of Statistical Thinking is to plan and implement various programs for statistical thinking,from extension courses to a professional development program. The researchers affiliated with the school are often
involved with specific data analysis projects, which help them to gain hands-on knowledge of data science. We expect
such an experienced researcher will play an active role as a number-cruncher, as a modeler, or as a project coordinator.

Research Support
Center for Engineering and Technical Support
The Center for Engineering and Technical Support assists academics and their collaborators in many ways:
managing computer systems and networks, editing and publishing journals, maintaining the library, and manage
extension courses.

■ Computing Facilities Unit ———————————————————————————————————————————————The Computing Facilities Unit is in charge of managing computer facilities, scientific software, networking infrastructure,and network security.

■ Information Resources Unit —————————————————————————————————————————————The Information Resources Unit is responsible for maintaining a library and an electronic repository, and is in charge ofplanning statistical education courses open to the public.

■ Media Development Unit ———————————————————————————————————————————————The Media Development Unit is in charge of publishing and editing of research results and PR brochures.

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Research Centers
Risk Analysis Research Center

How to Predict Future Population of Wild Animals?
N Fundamental problem
The importance of conservation of engaged species
is currently widely recognized. By the way, how can we
answer to the following question?
For bird species A, an adult female lays one egg every
year. It takes 3 years until a nestling grows to an adultwith 60% yearly mortality. The mortality of adults decreases to 5%. Is this species growing or declining?
For species B, an adult female lays 3 eggs. It needs 5
years until a nestling becomes an adult with 25% mortality. The adult mortality increases to 75%. Comparedwith species A, is species B growing more rapidly or
declining?
In order to increase the population, which is more
effective, increase the survival of nestlings or that of
adults?
N Transition matrix model
It is not easy to imagine the future of these populations. The Markov transition matrix model greatly helps
us solve these issues.
First, we illustratively draw a life history of the species (Fig. 1).
Second, based on the survival rates, we produce thematrix shown in the right in Fig. 2. Third, from the information on eggs, we produce the matrices shown in theleft in Fig. 2.

Finally, taking the product of the two matrix and calculating the eigenvalue of that matrix. It is known thatthe eigenvalue expresses the growth rate of that population.
In the case of the above examples, growth rates are
1.012 and 0.99 for species A and B, respectively. Therefore, we can predict that species A will be growing of
1.2% every year, while species B is declining of 1%.
N Estimation from data
In order to apply this mathematical model, we needestimates of survival and numbers of eggs. For the latter, we may count them in bird’s nests. For the former,
deploying a ring to indentify each individual is commonly done. If we check whether that individual is present every year, we can estimate the survival probability. Note that it needs at least 2 times censuses. If an
individual is not observed in the next census, there is
no way to distinguish whether that individual died or
we just failed to find it (Fig. 3b). Continuing 2 or moretimes, we will obtain data like Fig. 3c, then we are surethat this individual was living and we failed to find it inthat census. Combining a suitable statistical model, we
can obtain estimates about mortality.
An advanced technique such as sensitivity analysis
allows us to know which is more effective, increasing
survival of nestling or adults.
N Field survey, mathematics and statistical model

Figure 1:
The illustrative formulation of life history.

Figure 2: Transition matrix models for ( a ) bird A and ( b ) bird B.

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Combining field surveys, mathematics and statistical modeling, we can search better risk management
for engaged species.
Kenichiro Shimatani

Figure 3: The concept for estimating survival from mark-recapturedata.

Study in Activity of Neuron and Neural System, and Its Application
In order to scientifically capture the risks of social
and natural phenomena, it is necessary to grasp theobject as a system and model it appropriately. The
neural system is taken up as an example of complicated, various, and huge systems in our study. Then themodeling of activity of a neuron and systematization ofneural systems by synchronization are studied alongwith the applications.
N Activity of neurons and systematization ofneural group by synchronization
Based on the relation between an individual (a single
neuron) and a group (a neural group), We aim to clarify
the mechanism of neural system by analyzing the electrophysical dynamics with a viewpoint on the relationship between individuals (neurons) and the group(neural system).
(1) Mechanism of electro-chemical activation of a single neuron
A neuron generates electric potentials of a pulse ora plateau when its dendrite receives electric and chemical stimulations as the input, and then the generated
signals are transmitted to the axon terminals for electrical potential or chemical secretion to stimulate thenext cells (neurons or other tissues). This response isan active operation that consumes energy and works
as an amplifier or an oscillator. It is impossible to realize with the conventional passive circuit models which
consists of the elements of capacitors and resistors.
In this study, the neuron is modeled by three terminal active elements separated by liquid junctions (Fig.
1), and formulated by the equivalent circuit defined byclosed loop gain G and feedback ratio β. This research
was awarded as the Best Paper at International Conference on Neurology held in July 2012.

( 2 ) Synchronization and systematization of neuron set
by mutual injection
It is clarified that the neuron operates as a pulse oscillator from the basic
study in neural activity
described in (1), then themechanism of synchronous operation of theneuron group is also
clarified. It is known thatthe group of neurons oscillates at an average peFigure 2:
riod when the neurons
Synchronization of neuralare injected of the outgroup by mutual injection.
put each other, even if
each neuron has an individual period. From this analysis, we have formulated electrophysically and formallythat the nervous system is a synchronization system
realized by mutual injection of neuron output.
N Estimation of liquid level of fuel by microwave

measurement
Natural gas is expected to expand the effective usage
because carbon dioxide emissions per burning calorie
are less than petroleum. However, in the transport ofliquefied natural gas (LNG), the measurement is really
tough problem by conventional schemes because of theextremely small reflection.
In our study, highly accurate measurements are possible by using not only reflected waves on the liquidsurface but also transmitted waves that pass throughboth liquid media and air. And the relative informationof known objects as a reference position are also usedeffectively in the location estimation in neural systemsof living animals.
This method was applied
for the patent in 2013, andexhibited at Innovation Japan held in Tokyo Big Sight
in September, 2014.

Yumi Takizawa

Figure 1: Model of an active neuron.

Figure 3:
Liquid level measurementby microwave.

5Research and Development Center for Data Assimilation

Hardware Random Number Generation with Physical Phenomena
N Random number

Random number is defined a sequence of randomvariables. An example is a sequence of binary randomvariables with equal probability. We can get this random numbers form a result of finite number of honest
coin tossing.
N Random number generator

Methods to generate sequence of random variable
are called random number generators. The sequence
generated by an algorithm on the computer is called
pseudo random number. The most popular pseudo
random numbers have uniform distribution on [0, 1).
There are linear congruential method, M-sequence,
Mersenne Twister and so on. These random numbers
are not true random number. The random number
sequence generated by a physical phenomenon iscalled physical random number and can seem to betrue random number. The most popular phenomenon
which is used for a hardware random number generator is thermal noise of electronic circuit. Recently chaos phenomenon of semiconductor laser or magnetoresistive element is used.
N Development of hardware random number

generator in our instituteWe introduced FACOM128 which is the first commercial computer system of Japan in 1956. We used

Figure 1: Component of hardware random number generator for
Hitachi’s computer M680H.
(1989, Generation speed is 1.5MB/s )6

hardware random generator whose noise source wasradioactivity. In 1963, 1971, 1989 we introduced hardware random number generator whose noise sources
are thermal noise of diodes and noise signal is digitized by using comparators. Fig.1 is a component of
hardware random number generator which was developed in 1989. Generation speed is 1.5 MB/s by using 8 boards in parallel. In 1998 we changed generation method. A/D converters are used for digitizing
analogue signal. Fig.2 is the generation board which
was developed in 1999 whose speed is 25MB/s. Speedof the latest board is faster than 640MB/s. These
hardware random number generators were certified
as historical computer equipment by Information
Processing Society of Japan in March 2016.
N Use of random numbers

Random numbers are used for Monte Carlo method, bootstrap and so on. Large-scale computations
are necessary to large-scale problems. Parallel computations must be conducted. We, however, cannot
accept results free from care, since it is too difficult
to confirm whether two random sequences have correlation or not. On the other hand we can use random numbers generated by hardware random number generator for parallel computing at ease. Users
can download random numbers from our portal site
http://random.ism.ac.jp/.
Yoshiyasu Tamura

Figure 2: Hardware random number generator for Hitachi’s supercomputer SR8000.
(1999, Generation speed is 25MB/s )Research Centers

Ensemble Prediction and Estimation of Probability Density
N Ensemble prediction

Ensemble prediction is a method of statistical prediction based on various results from numerical simulation models that have different configurations suchas initial and boundary conditions, input parameters,
and sub models. A set of the various results is called
an ensemble, and each result is called an ensemble
member. Based on statistics such as mean and variance of the ensemble members, ensemble prediction
estimates the most probable event and its occurrence probability.
A role of each ensemble member is to represent an
event depending on its configuration. Furthermore,each member may be generated from different simulation models. Different simulation models enhancediversity of the ensemble members, and reproduce ensemble members that are likely to occur. Such an ensemble generated by multiple simulation models iscalled a multi-model ensemble.
N Estimation of probability density

What should we do for statistical inference basedon an ensemble? The first step may be to take an average of the ensemble members, but is it reasonable?
Taking an average is equivalent to one model one vote.
Since each simulation model has one vote regardlessof the goodness of the model, a good result may beburied in results that seem to be questionable. This is,of course, the situation we would like to avoid.
We now consider a way to improve ensemble prediction using observation data. While combination ofensemble simulation and observation may evoke amethod of ensemble data assimilation such as theensemble Kalman filter and the particle filter, thesituation is not actually quite straightforward. Application of the methods of data assimilation requires
specification of error parameters of the ensemble
members, and more importantly, requires a restart
process of temporal integration after replacing statevariables. The latter requirement is difficult to satisfy
especially for multi-model ensembles in a practicalsense.
We then propose a method for density estimation
using a regression model in which observation is explained by the ensemble members. The regression
model can estimate the error parameters automatically and we do not need to specify in advance. Furthermore, we can select useful simulation models byintroducing a concept of sparsity in the cost function.
Consequently, we can discard a model that does not
additional explanation abilities and we can estimate
probability density considering the effect of redundancy of multiple simulation models.
Genta Ueno

Figure: Probabilistic climate projections for surface air temperature for the end of the 21st century using theregression model. ( provided by National Research Institute for Earth Science and Disaster Resilience )7

Research Center for Statistical Machine Learning

Polynomial-time Algorithm for Some Non-convex Optimization Problems
N Difficulty of non-convex problem

Mathematical optimization problem is to achieve
better object under given constraints. If the problem
objective function and constraints are all represented by convex functions, it is called convex problem.
The Karush-Kuhn-Tucker (KKT) condition is a necessary and sufficient condition for the optimal solution
if it is a convex problem. On the other hand, for nonconvex problems, the KKT condition is not a sufficient condition, and local optimal solutions and stationary points also satisfy the KKT condition. So it isgenerally difficult to find a global optimal solution for
non-convex problems.

Figure 1: Convex optimization and nonconvex optimization.

In recent years, we have seen non-convex problems in various fields. For example, a non-convex binary classification model Eν-SVM is also proposed.
As solution methods for solving convex optimization
problems have been established, research target has
gradually shifted to non-convex optimization.
N Polynomial-time algorithm for non-convex

classification model
When the average vectors of the two classes areclose and the number of data is extremely different
between the two classes, the range of the hyper-parameter of usual SVM is narrowed and good prediction can not be done. Even in such a case, EvSVM can select hyper-parameter values ​​froma wide range by sacrificing convexity, andconsequently leads to good predictive performance. We mimicked Eν-SVM and developed
non-convex variants of other classification
models (Iwata, Nakatsukasa, Takeda; 2014). In
order to obtain the decision rule, it is necessary to solve the non-convex quadratic problem, but the global optimal solution can beobtained with polynomial time. The algorithm
is very simple; after enumerating all the solutions that satisfy the KKT condition by eigenvalue calculation, choose the one that achieves
the smallest function value.
N Polynomial-time solvability of

Figure 2: Extending binary classification models to nonconvex ones.

min x Τ Qx + 2q Τ xxs.t. ||x || 2 ≤ s 2 , || AΤ x + c|| 2 ≤ u2
Figure 3: Celis-Dennis-Tapia ( CDT ) problem.

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Celis-Dennis-Tapia (CDT ) problem
Furthermore, we showed that the proposed
method can be applied to the CDT problem(the problem of non-convex quadratic function minimization under two convex quadratic constraints) by adding a little ingenuity(Sakaue, Nakatsukasa, Takeda, Iwata; 2016).
Since the CDT problem was proposed in 1985,
its polynomial-time solvability has been studied. Just before submitting the paper, Bienstock proposed a polynomial-time algorithmof the CDT problem (Bienstock; 2016). However, his method includes procedures that are considered to be difficult to implement. Therefore, ourresearch has shown a solution method that can be
implemented for the first time for the CDT problem.
Akiko Takeda

Research Centers

Anonymizing Location Data Based on the Space-state Model
N Anonymization of location data

Nowadays, trajectory location data, whichis collected from people’s smart phones, canbe used for various analytic purposes, suchas traffic monitoring, urban city planning.
However, due to significant concern aboutlocation privacy, location data must be anonymized properly before making it available
for secondary usage, as shown in Figure 1.

Figure 1: Privacy-preserving data publishing of location data.

N Issues in k-anonymization

To address the issue of location privacy, we
usually adopt a technique called k-anonymization, which ensures that every location inan anonymized dataset contains more than kpeople. Figure 2 shows the location trajectories of two users with grid IDs. When we usecoarser granularity of location data, which isshown with red rectangles, we obtain 2 -anonymized location data.
However, since trajectory location data inFigure 2: An example of 2-anonymized location data.
volves strong temporal and spatial correlations among data points, it is often possible
to restore original location data from aonymized one.
Figure 3 shows 2 -anonymized location data of thetwo users driving a car. Although the granularity oflocation data is generalized to achieve 2-anonymity,
it is trivial to infer the exact locations of the two drivers if we learn the road model, which is shown withdotted lines.

Figure 3:
Inference of original
location data using
map information.

N Anonymization model based on the hidden

Markov model
We model users’ mobility patterns based on theMarkov chain and evaluate the safety of an anony­mized dataset based on the hidden Markov model,that is, the disclosure risk is formulated as the condi-

Figure 4: Anonymization model based on the hidden Markov model.

tional probability of inferring the sensitive internal
state, given externally observable information. Figure
4 describes our framework for evaluating the safetyof an anonymized dataset. The trajectory of internal
states corresponds to the original location
data, while observation data corresponds
to the anonymized data.
Our experimental results with a real dataset show that the metrics of k-anonymization is not often sufficient to ensure expected safety property, and that it is necessary
to additionally suppress non-sensitive location points to achieve the desired safety.
Kazuhiro Minami

9Data Science Center for Creative Design and Manufacturing

Data Science Center for Creative Design and Manufacturing
N Strategic goals
We aim to motivate advanced technologies in the field of
data science, and foster scientific methodologies for innovative design and manufacturing. Various fields in design
and manufacturing are a major part of our country’s fundamental industry. They are now facing a revolutionary period. Population reduction and globalization change the current industrial structure in Japan, resulting in a rapid lostof our global predominance in the manufacturing industry.
Furthermore, countries around the world has actively
launched new data science programs for the next generation design and manufacturing, such as, the Materials Genome Initiative in the US and Industry 4.0. Following the
global trend is no longer an effective way to survive in theintensive power game around the world. We have to develop
innovative thinking, for which data science can provide theessential tools. The Institute of Statistical Mathematics has
decided to establish a data science research center for the
purpose of creative design and manufacturing. We have accumulated state-of-the-art data science technologies here,
for instance, machine learning, optimization theory, data
assimilation, Bayesian inference, materials informatics, etc.
We are devoted to foster innovative methods for design andmanufacturing based on co-created values through industry-academia collaboration. This is the mission of this center and our keywords are “smart” and “creative design andmanufacturing”.

N Smart manufacturing
Current approach of material design relies on a classicalcycle: initial guess based on researcher’s intuition and ex-perience, property estimation using large scale simulationand experiment, revision of the design based on the results.
Such a process requires a large amount of time and financial costs to discover and produce a new material. On theother hand, recently, there are new attempts to substitutethe heavy simulation and experiment by statistical models
based on many existing data. This approach will achieve
extremely efficient estimation of the material properties.
The enormous cost and time required in a property test has
limited the new material discovery within a small set of candidates. High throughput screening using data science may
significantly increase the probability of new breakthroughs
in material science. Application of such advanced methods
to the traditional design and manufacturing process will
drastically reduce the R&D cost and time. This is the basic
concept of smart manufacturing in the perspective of data
science.

N Creative design and manufacturing
We recognize the importance of being at the absoluteleading edge position in the manufacturing industry. Thiscannot be done by data science alone. Most of the data science analysis tools are designed for interpolative predictions. For example, we often assume that materials withsimilar chemical structure exhibit similar physical properties. However, by definition, new material is not likely to be
similar to any of the existing materials. Combination of experiment, theory and data science methods is an essential
step to a new breakthrough in the current state. In otherwords, we adopt a stepwise approach to expand the regionof accurate prediction of a statistical model. We achieve thegoal by careful design of an optimal
experiment or simulation schedule
for new data points that efficiently
improve an existing predictive model. We have accomplished preliminary success in material science
using an extrapolative prediction
method based on a data science approach, and fostered new collaboration opportunities between academia and industry for the purposeof new material discovery. Our next
step is to extend the application tovarious fields related to creative design and manufacturing.

Ryo Yoshida

Figure: Smart manufacturing in the perspective of data science.

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Research Centers

Towards Data-driven Materials Discovery
With state-of-the-art technologies in data science, we
aim to discover novel functional materials. Target materials include drugs, dyes, solvents, polymers, polymericcomposites and nanostructured materials. With thecomprehensive technologies of machine learning, suchas Bayesian modeling, kernel methods, natural language
processing, sparse learning and optimization theory, we
create the fundamental methodology and research infrastructures of Materials Informatics.

N Materials Research by Information Integration

Initiative ( MI2I )Materials Informatics is an emerging cross-disciplinary
field aimed at combining materials science and data science. The Materials Genome Initiative (MGI), announced
by President Obama in 2011, was designed to create infrastructures that accelerate the pace of discovery anddeployment of new materials for innovative products
twice as fast. The MGI white paper has stated Materials
Informatics as the key to achieving dramatic rationalization of time and costs in developments. In Japan, the Japan Science and Technology Agency (JST) launched theMaterials Research by Information Integration Initiative(MI 2I) at National Institute of Materials Science (NIMS)
on July 2015. The Institute of Statistical Mathematics has
been designated to be a recommitment site of MI 2I as thecentral institute of data science in Japan.

N Role of data science in material discovery anddevelopment
The design space of materials development is considerably high-dimensional. For instance, the chemical spaceof organic compounds consists of 10 60 potential candidates. The challenge is to discover unidentified novel
materials from the huge landscape that exhibit desirable
material properties. In the traditional procedure, computational chemistry methods, such as the first principle
calculation, have been put at the central analytic tool.
Scientists hypothesize material structures based on experience and intuition, and properties of the designed
materials are assessed computationally and experimentally. The data-centric approach has been paid much attention as a promising alternative that can promote enormous savings on time and costs in the laborious andtime-consuming trial-and-error procedure.

N Bayesian approach to data-driven materials

discovery
The aim of our study is to create a novel material design method by the integration of machine learning andquantum chemistry calculation. The method begins by
obtaining a set of machine learning models to forwardly
predict properties of input material structures for multiple design objectives. These models are inverted to thebackward model through the Bayes’ law. Then we have aposterior probability distribution which is conditioned
by desired properties. Exploring high probability regions of the posterior, it isexpected to identify newmaterials possessing the desired target properties. Under industry-academia partnerships, we are putting into
practice this Bayesian material design method.

Ryo Yoshida

Figure: Material discovery algorithm: SPACIER.

11

NOE ( Network Of Excellence ) Project
Building a Framework for Advancing Strategic Research andPursuing a New Approach to Collaborative Research
N Research and Educational Activities asa Biaxial Structure

The Institute of Statistical Mathematics (ISM) pursues
research and education along the two lines of basic research, and NOE (Network Of Excellence)-type researchand professional development. These are respectively
conducted by the basic research departments along a
horizontal axis, and the NOE-type research centers andthe school for professional development along a verticalaxis (Figure 1). By its nature, the basic research departments (along the horizontal axis) cut across and linkvarious disciplines, developing tools for interdisciplinary research. The field of statistical mathematics must
itself evolve to meet the changing needs of society andthe data environment, and is therefore constantly evolving as a field of study. At the same time, there are approaches and directions that remain unchanged as thefield evolves. For that reason, we have chosen not to call
it fundamental research or foundational research but“basic research”, to reflect both the fixed and evolving
qualities of statistical mathematics. There are three basic research departments: “Statistical Modeling”, “DataScience”, and “Mathematical Analysis and Statistical Inference”. These departments engage in cutting-edge research to develop methodologies for rational prediction
and decision making, based on data and existing knowledge. All tenured research staff members in ISM are assigned to one of these basic research departments. However, the time has come to reconsider “basic research”
for the reason already stated — statistical mathematics
must itself evolve to meet the changing needs of society
and the data environment — and so we are planning toreorganize the basic research departments. The new organizations will be in place as of April 2018.

Figure 1: Biaxial structure for research and education

12

On the other hand, the NOE-type research centers
and the school for professional development (along thevertical axis) are staffed by permanent researchers within ISM, project professors/researchers (post-doctoral
staff), and visiting professors and researchers. We reorganized the NOE-type research centers for the previous
FY2016. There are currently four NOE-type research centers, “Risk Analysis Research Center”, “Research and Development Center for Data Assimilation”, “Research Center for Statistical Machine Learning”, and, set up on July 1,
2017, “Data Science Center for Creative Design and Manufacturing”. These centers conduct research activities thatinterface statistical mathematics with individual scientific disciplines in order to find solutions to urgent social
problems.
There is also the School of Statistical Thinking in ISM
as a school for professional development, fostering andpromoting statistical thinking in multiple programs of the
project. In the School of Statistical Thinking, researchers, students, and contract researchers from privatecompanies who have an eye on the creation of a newstatistical research field, as well as various other people
who realize the necessity of statistics in their particularresearch field, train together to foster statistical thinking. Of special importance, young project researchers
receive on-the-job training from their senior mentors
and thereby obtain the assorted skills of “statisticalthinking”. We also established the “Managing Committee of School of Statistical Thinking”, whose members
are experts in professional development from private
industry and academia outside ISM. Please refer to thepage “Project for Fostering and Promoting StatisticalThinking” for more details.

ISM Projects

N NOE ( Network Of Excellence) ProjectIn accordance with the second medium-term plan of
Research Organization of Information and Systems (ROIS,
ISM’s parent organization), ISM had set as a goal the establishment of NOEs (Networks Of Excellence) in statistical mathematics. With the formation of an NOE in each of
five research fields —“Risk Research”, “Next-Generation
Simulation”, “Survey Science”, “Statistical Machine Learning”, and “Service Science” — we have been conducting
joint research activities that serve to deepen collaborative relationships with other institutes, both within Japanand overseas, for these last six years.
The previous FY2016 was the very first year of thethird medium-term plan. We made a self-evaluation of
the six years of the second medium-term plan and thenreorganized the structure for managing the NOE Project
and NOE-type centers. We closed the Survey Science
Center and the Service Science Research Center. The
projects of the Survey Science Center have matured andtransferred to the “Joint Support-Center for Data Science” of ROIS. However, ISM will continue the study of
the Japanese National Character as one important areaof the Institute’s research. Also, the “Survey Science
Group” in the Department of Data Science is now thecore of the Survey Science NOE. The activities of the Service Science NOE finished on January 31, 2017, and we
transferred the projects of the former Service Science
Research Center to other NOE-type centers according
to their methodologies.
We set up a new NOE-type center, “Data Science Center for Creative Design and Manufacturing”. As of the current FY2017, the research activities in the NOEs are Risk
Research, Next-Generation Simulation, Survey Science,
Statistical Machine Learning, and Data Science for Creative Design and Manufacturing.

N Future Conception of NOE Activities
To fulfill the goal of establishing the new scientificmethodologies (“Fourth Paradigm”) in a knowledgebased society, in which the importance of knowledge
goes beyond merely solving individual problems, NOE
activities are being systematically pursued under theunified project guidelines decided by the “Managing
Committee of NOE Project”. At the same time, advice isalso taken from the members of the “Advisory Board of
NOE Project” (see Table 1), made up of experts from theindustrial, academic, and government sectors.
The NOE Project, which is made possible by ISM’s
special focus in the cross-disciplinary field of “statisticalmathematics”, is attracting strong support from each ofthese communities. On the basis of this project, ISM, asan Inter-University Research Institute, will be providing
further opportunities to the industrial, academic, andgovernment communities for joint usage (of facilities)
and joint research. ISM decided to continue to promote
this NOE Project during the third medium-term plan of
ROIS, which has gone into effect as of April 2016.
As described above, the NOE research centers serveas core hubs for their respective fields. ISM is promoting
the signing of MOUs (Memorandums of Understanding)with research organizations within Japan and overseas,
and the number of MOUs is increasing year by year, including MOUs which transit multiple NOE research fields.
The goal of ISM is general research in statistical mathematics, which is in demand by various research fields,
both in the humanities and science. ISM has to respondflexibly to requests from each community and contributeto them. Reorganization from last year to this year was
based on the needs of each community and is for the
purpose of more strongly promoting the NOE Project.
With its focus on methodology, ISM continues to plan
research in each of the NOE research fields. Furthermore,with all research activities concentrated under the umbrella of the NOE Research Promotion Organization, thegeneral body that oversees the five NOEs, ISM is expanding and developing its NOE projects with the aim ofestablishing new scientific methodologies (“Fourth Paradigm”), creating new research disciplines, and developing new styles of joint research. For the specifics of the
new research structures of the NOE Project and more
up-to-date information, please visit the website. We greatly appreciate your continued support and understanding
for this Project.

http://www.ism.ac.jp/noe/project/en/

Director of the Center for
Materials Research by Information
Integration, MDIS, National
Institute for Materials Science

Dr. Satoshi Itoh

Professor Emeritus,
Tokyo Institute of Technology

Dr. Takatoshi Imai

Adjunct Professor, Computational
Science Education Center,
Kobe University

Dr. Yoshio Oyanagi

President, The Japan
Pharmaceutical Manufacturers
Association

Mr. Yoshihiko Hatanaka

President, Toyota Technological
Institute at Chicago

Dr. Sadaoki Furui

Director-General, Institute for
Monetary and Economic Studies,Bank of JapanMr. Shigenori Hiratsuka

Table 1: Members of Advisory Board of “NOE Project”( As of April 1, 2017 )13

Project for Fostering and Promoting Statistical Thinking

Rapid development of information and communication technology has led to the explosion of data.
Now surrounded by “Big Data”, everybody is expected to “think statistically”. More than ever, there is aneed for data scientists who can handle such big data and are able to extract useful knowledge from it.
Meanwhile, Japanese higher education is exhibiting a deplorable lack of production capacity in terms ofdata scientists. This can be accounted for by the fact that no academic institution other than ISM has adepartment of statistics and the small number of statisticians in academia are isolated from each other,
being scattered over various disciplines. Hoping to gain a little traction on this problem, ISM establishedthe School of Statistical Thinking, into which we integrated all of our educational resources. The following
are the principal projects.

Research Collaboration Start-up
The Institute had already been providing a consultation service for statistical science, but alongwith the launch of the School of Statistical Thinking in November 2011, this service was reorganized
as a research collaboration start-up. This program,being one of the projects to foster and promotestatistical thinking, is mainly aimed at supporting
applied scientists and other non-experts. Expert
statisticians affiliated with the Institute give them
advice on statistical modeling, data analysis, andresearch. Some cases have developed into official
research collaborations, which are our primary
duty as an inter-university research institute. The
start-up program covers a variety of topics, ranging from introductory to more specialized studies.
Half of the clients are from the private sector, andthe rest are staff members of public organizations,

university professors, or students. The Institute accepts more then 50 cases annually, some of which
benefit society in diverse ways.

Open-type Professional Development ProgramThis is a spin-out program from ISM cooperative
research projects. Establishing a goal is an indispensable element of the proposal of a cooperative
research project. On the other hand, such goal
setting is irrelevant for a summer school program,study session, or retreat. Since the launch of the
School of Statistical Thinking, organizers of such

14group-oriented study programs can apply to theOpen-type Professional Development Program.
There are two categories under this program: oneis ‘workshop’ and the other is ‘intensive training foryoung researchers’. For FY 2017, five workshops
have been accepted after review.

ISM Projects

Statistical Mathematics Seminar Series
The Institute holds weekly seminar series on statistical mathematics every Wednesday. The seminars are led by in-house and external lecturers to
showcase their latest studies. These lectures arefree to attend. To view the seminar schedule andlearn more about the program, please visit the Institute of Statistical Mathematics website.
http://www.ism.ac.jp/index_e.html

Open Lecture
We hold an open lecture during Education andCulture Week every year (November 1 through7), to introduce the Institute’s activities and topromote statistical science. We invite lecturers
to speak on a timely topic relating to statisticalscience. The lecture is open to the general public.
For further information, please visit the website of
the Institute of Statistical Mathematics.
http://www.ism.ac.jp/kouenkai/

Tutorial Courses
The education program at ISM dates back to
1944, the year of founding. The Ministry of Education installed a training center within ISM to foster
technicians in numerical computation. After theWorld War II, this training center was relaunched
in 1947 to develop pollsters and census takers. It
helped to cultivate professionals in the field of statistical surveys, while a growing number of entries
from business and industry coming for various
types of training were also observed.
When ISM joined the Inter-University ResearchInstitute Corporation in 1985, the training center
was formally shut down. We nevertheless tried tomaintain 4 courses per year, relying on support
from the private sector. The number of courses
was tripled when ISM became an independentagency in 2005. Now the tutorial courses are operated by the School of Statistical Thinking, which
was established in 2011.
In the 2016 academic year, 14 courses were held
and the number of participants was 968. The total

number of courses held from 1969 to March, 2017
was 349, with a total of 24,925 participants. These
courses covered a wide range of fields from basic toapplied statistics. The schedule of tutorial courses
can be found on the website of the Institute of
Statistical Mathematics.
http://www.ism.ac.jp/lectures/kouza.html

15

Producing Data Scientists in the Big Data Era

ISM Projects

Innovations in Our Data Science Program and Business-Academia
Collaboration
N Data Scientist Crash Course
To maintain an efficient eco-system for producing data scientists, the system should contain aproper balance of data scientists of various levels.
In this regard, it has been said that Japan should
increase its number of leading data scientists (comparable to JSSC class 1 qualifiers). As an attempt
to fill the current gap, ISM held the “Statistical Machine Learning Crash Course”, which was co-hosted by the RIKEN Center for Advanced Intelligence
Project (AIP). This course consisted of six lectures,

Data Scientist Crash Course

and was held on February 9 and March 7 and 9,
2017. All the instructors were ISM researchers who
were leading scientists in the area of statistical machine learning. Lectures covered (1) information
processing with sparsity, (2) pattern recognition,(3) materials informatics, (4) statistical machinelearning for discrete data, (5) optimization methods under uncertainty, and (6) nonlinear data
analysis by the kernel method.
N Data Science Research Plaza

Researchers funded by private-sector firms canmaintain a desk and phone in the School of Statistical Thinking. This program is subject to fees, andthe contract can be renewed annually. Supercomputer access is available to them as long as theysubmit their proposals to High Performance Computing Infrastructure; see the web site below. A
faculty mentor gives advice to the accepted funded
researcher so that he or she can freely attend various events, such as seminars, workshops, conferences, and extension courses. After learning theexpertise of the ISM research staff, participants inthis program are invited to take advantage of paid
consultants and funded research collaboration.
(http://www.hpci-office.jp/folders/english)

N Data Science Course Entrusted on theBasis of Organizational Cooperation

For universities and institutions who have an
MOU with ISM, or some other organizational agreement on mutual research/educational cooperation,
ISM is ready to offer a data science course plan.
These universities and institutions entrust the data
science course plan (including possible lecturers)
to ISM, and ISM proposes a plan based on theirrequest. By exploiting the deep knowledge, expertise, and academic network of ISM professors, weplan an appropriate combination of course topics
and instructors so that they fit each university’s
educational goal, whether the level is undergraduate or graduate.

Conceptual drawing of entrusting a data science course

16Cooperation with Mathematics Program

ISM Projects

Towards the Formation of Network-based Research Foundations for Collaboration
between Mathematical Science, and Various Scientific Fields and Industries
N What is the Cooperation with

Mathematics Program?

The Cooperation with Mathematics Program is
a MEXT-funded project that was planned in an atmosphere of promoting research via collaborations
between mathematics and other scientific fields, aswell as industry. The aim of this Program is to provide mathematicians and researchers in various
scientific fields and industries with opportunities
for inter-field discussions, and thus, to promote thefinding of solutions to challenging problems via
collaborations. The Program was started in 2012as a five-year project entrusted to The Institute of
Statistical Mathematics. The Institute has from the
beginning managed the following activities along
six main themes decided by the steering committee
under the Program with the aid of three related academic societies and nine academic institutions, including three joint usage/research centers on mathematical science.

N Activities of the Cooperation with

Mathematics Program

Visible activities of the Program include supporting and organizing workshops, study groups, working groups, and tutorial seminars, as well as conducting outreach activities for the general public.
The workshops aimed to discover new or challenging problems, which it is hoped will be solvedby the collaboration of various scientific fields
and industries with mathematical science. Studygroups, which historically first appeared at Oxford

University in 1968, differ from workshops. Generally, coordinators start by providing several problems from scientific fields or industry and each participant chooses one of these and concentrates onsolving it for about a week. At the end of a study
group, participants who worked on each problem
give presentations about solutions or proposals
for solutions to the problem. During its five years,
the Program conducted 75 workshops and 27 study
groups (11 focused on scientific fields and 16 focused on particular industries).
The Program independently supported finding
problems in specific fields that were expected tobe solved by mathematicians, through organizing
working groups consisting of mathematicians, statisticians, and engineers in the specific field. Suchworking groups were established under the Program in the three fields of material science, life science, and finance. Outcomes from these working
groups’ activities include publishing proposals forcollaborations between mathematical science andthese specific fields, which are available from our
website shown below.
Tutorial seminars on specific themes meetingthe demands from other scientific fields or industries were organized either independently or inworkshops or study groups. The Program also supported series of tutorials organized by the National Institute of Materials Science or by the Society ofAutomotive Engineers of Japan, Inc.
In addition, the Program organized or supported
various outreach activities. These include careerpath seminars for graduate students or young researchers majoring in mathematical science, a series of talk-exhibition events at JST Science Agora
for the general public, and disseminating related
information through monthly mail magazines andSNSs such as Facebook and Twitter.
Detailed information about the Program can beaccessed through its website (http://coop-math.
ism.ac.jp), Facebook (https://www.facebook.com/
CoopMath), and Twitter (@CoopMath).

Panel discussion in a joint symposium held withJST CREST/PRESTO programs

17

Research Cooperation

International Cooperation■ Associated Foreign Research Institutes

18

Organization name

Address

Conclusion day

The Statistical Research Division of the U.S. Bureau of the Census

USA ( Washington )July 27, 1988

Stichting Mathematisch Centrum

The Kingdom of the Netherlands( Amsterdam )May 10, 1989

Institute for Statistics and Econometrics, Humboldt University of Berlin

Germany ( Berlin )December 8, 2004

Institute of Statistical Science, Academia Sinica

Taiwan ( Taipei )June 30, 2005

The Steklov Mathematical InstituteRussia ( Moscow )August 9, 2005

Central South University

China ( Changsha )November 18, 2005

Soongsil University

The Republic of Korea ( Seoul )April 27, 2006

Department of Statistics, University of Warwick

The United Kingdom ( Coventry )January 16, 2007

The Indian Statistical Institute

India (Kolkata )October 11, 2007

Department of Empirical Inference,
Max Planck Institute for Biological Cybernetics

Germany ( Tubingen )August 11, 2010

Department of Communication Systems,
SINTEF Information and Communication Technology

Norway ( Trondheim )January 30, 2012

Human Language Technology Department, Institute for Infocomm Research

Singapore (Singapore )February 16, 2012

Centre for Computational Statistics and Machine Learning,
University College London

The United Kingdom ( London )February 16, 2012

Department of Electronics and Telecommunications,
Norwegian University of Science and Technology

Norway ( Trondheim )May 22, 2012

Department of Probability and Mathematical Statistics,
Charles University in Prague

Czech Republic (Prague )October 10, 2012

The Department of Ecoinformatics,
Biometrics and Forest Growth of the Georg-August University of Goettingen

Germany (Goettingen )October 18, 2012

The Korean Statistical Society

The Republic of Korea ( Seoul )July 9, 2013

Toyota Technological Institute at Chicago

USA ( Chicago )February 10, 2014

Mathematical Sciences Institute Australian National University

Australia ( Canberra )May 15, 2014

RiskLab ETH Zurich

Switzerland ( Zurich )February 7, 2015

Institut de Recherche en Composants logiciel et materiel pour l’Information
et la Communication Avancee (IRCICA)

France ( Paris )February 9, 2015

Le laboratoire de mathematiques de I’Universite Blaise Pascal

France (Clermont-Ferrand )February 11, 2015

Centre de Rechereche en Informatique,
Signal et Automatique de Lille (CRIStAL) UMR CNRS 9189

France ( Paris )February 12, 2015

University College London (UCL) Big Data Institute

The United Kingdom ( London )February 26, 2015

The Institute of Forestry, Pokhara of Tribhuvan University

Nepal ( Pokhara )March 6, 2015

The Institute of Forest and Wildlife Research and Developmentof the Forestry Administration of Cambodia

Cambodia ( Phnom Penh )March 6, 2015

The Chancellor masters and Scholars of the University of Oxford

The United Kingdom ( Oxford )March 10, 2015

Forest Inventory and Planning Institute of Vietnam

Vietnam (Hanoi )June 2, 2015

Zuse Institute Berlin

Germany (Berlin )June 20, 2016

The University of Porto

Portugal ( Porto )June 22, 2016

Natinonal University of Laos

Laos ( Vientiane )March 15, 2017

Institute of Geophysics China Earthquake Administration

China ( Beijing )April 28, 2017

Research Collaboration
ISM performs many activities for collaborating with researchers in the various fields of statistical science,from the individual level to the national level. The ISM cooperative research program regularly performs research activities to provide the various research resources of ISM to researchers at universities or research
institutes in order to advance their academic research. Available research resources include books, journals,
supercomputers, some commercial statistical software packages, as well as statistical packages developed by
ISM, and also the researchers in ISM themselves, who have abundant professional knowledge and experiencein statistical science and data analysis. The ISM cooperative research program provides not only researchsupport funds but also opportunities for the various researchers in many fields who require statistical knowledge to make use of the resources available at ISM. ISM’s aim is to be a place for interaction and fusion among
researchers inside and outside of ISM, and to contribute to multidisciplinary development of both the theory
and the application of statistical science.

■ Number of Activities
2011

2012

2013

2014

2015

2016

172

182

181

177

183

187

■ Fields of Research Collaboration
Research collaboration is classified by research field as follows. Applicants can use the table below to findthe most appropriate type of project.
ISM Fields
NumberFieldsNumberFieldsa

Spatial and Time Series Modeling Groupf

Structure Exploration Groupb

Complex System Modeling Groupg

Mathematical Statistics Groupc

Latent Structure Modeling Grouph

Learning and Inference Groupd

Survey Science Groupi

Computational Inference Groupe

Metric Science Groupj

OthersMajor Research Fields
NumberFieldsMajor Research Domains

1

Statistical mathematics

Mathematical theory of statistics, optimization, etc.

2Information science

Algorithms, use of computer in statistics, etc.

3

Biological science

Medicine, pharmacy, epidemiology, genetics, etc.

4Physical science

Space, planet, earth, polar region, materials, etc.

5Engineering

Mechanics, electronics, control, chemistry, architecture, etc.

6

Human science

Philosophy, art, psychology, education, history, geography, culture, language, etc.

7Social science

Economics, law, politics, society, manage­ment, official statistics, population, etc.

8

Environmental science

Environmental Statistics, Environmentrics, Agricultural Statistics, Statistical Meteorology,
Land Economics, Landscape Management, Forest Management, etc.

9

OthersOther research fields

19Graduate School Program

Organization
The Institute of Statistical Mathematics is one of the platforms of SOKENDAI (The Graduate University for
Advanced Studies; the headquarters in Hayama, Kanagawa), which was opened in October 1988 to offer graduate education. Since its opening, the Institute has included the Department of Statistical Science and, sinceApril 1989, has accepted students for education and research in doctoral programs. In 2006, the Instituteadopted a five-year system, offering either a five-year education and research program, or a three-year education and research program starting from the third year of study.
Regional Studies
Comparative Studies

School of Cultural and Social Studies

Japanese Studies
Japanese History
Japanese Literature
Structural Molecular Science
Functional Molecular Science

School of Physical Sciences

Astronomical Science
Fusion Science
Space and Astronautical Science

SOKENDAI

Accelerator Science

School of High Energy Accelerator Science

Materials Structure Science
Particle and Nuclear Physics
Statistical Science

School of Multidisciplinary Sciences

Polar Science
Informatics
Genetics

School of Life Science

Basic Biology
Physiological Sciences

School of Advanced Sciences

Evolutionary Studies of Biosystems

Outline of Education and Research
The Department of Statistical Science, which is based on the Institute of Statistical Mathematics (ISM) serving as
its underlying platform, intends to cultivate individuals who possess creative research and educational skills andcontribute to solving various important intricately-intertwined issues through extraction of information and knowledge from the real world taking advantage of their skills in modeling, prediction, inference, and collection of data,
while conducting research and education on their foundations, i.e., mathematics, computation, and applications.

20

Field of Education and ResearchContents

Statistical Modeling

Education and research focuses on dynamic modeling such as spatial and space-time modeling, graphicalmodeling of temporally and/or spatially inter-related complex phenomena, and intelligent information
processing. We also provide education and research on statistical inference based on various models,
methods of calculation for inference, and evaluation of models based on data.

Data Science

We provide education and research on data design, investigation and analytical methods to cope with theuncertainty and incompleteness of information, as well as on computational statistics.

Mathematical andStatistical Inference

We provide education and research on the theory of statistics and related basic mathematics, statistical learningtheory to extract information from data by automated learning and inference techniques; as well as theory andapplications of optimization and computation algorithms which serve as the basis for computational inference.

Features of Education and Research● The course is the only integrated doctoral program on statistical science in Japan. It has received students

from a wide variety of disciplines and has offered education and research on statistical science by professors specialized in many different fields, from theory through to practical applications.

● The Institute of Statistical Mathematics, the platform for the course, is equipped with a world-class supercomputer, high-speed 3D graphic computers and simulators to generate physical random numbers, as well
as a variety of software, including original statistical software developed by the Institute.

● The academic publications and books on statistical and mathematical sciences produced are some of thebest in the world.

● In its role as an inter-university research institute, the Institute holds frequent workshops and seminars byvisiting professors and researchers from both Japan and abroad. Students are free to attend and participate.

● It is possible to collaborate with researchers from other universities and institutions. It is also possible forstudents to develop their own projects by participating in research projects with other institutions throughthe Transdisciplinary Research Integration Center, Research Organization of Information and Systems.

Course Requirements and Type of Degree Granted● Requirements to complete the doctoral course are as follows:

Completion of at least 40 credits, including the required ones, by a student in the five-year program, or completion of at least 10 credits, including the required ones, by a three-year doctorate student who previously
completed a Master’s course; meeting all the criteria set by the thesis committee of the Institute; and successfully completing the final examination.

● On completion of the course, either a Doctorate in Statistical Science or, if the thesis deals mainly with aninter-disciplinary field related to statistical science, a Doctorate of Philosophy is awarded.

● The required number of years of study will be flexible if a student demonstrates outstanding research results.

Number of Students ( As of April 1, 2017 )■ Doctor’s course five years: Quota,2Year of enrollment
Number of students■ Doctor’s course three years: Quota,32010 2013 2014 2016
1

2

2

1

2017

Year of enrollment

20112012 2013 2014 2015 2016

2017

1

Number of students

1

1

3

1

5

2

5

* The figures in circles indicate those who are employed by other organizations.

University Background of Students
National and public universities• Hokkaido University ( 4 ) • Tohoku University ( 3 ) • Fukushima University (1) • University of Tsukuba (6 ) • Saitama University (1)• Chiba University (1) • Ochanomizu University (1) • National Graduate Institute for Policy Studies (1) • Tokyo Medical and Dental
University (1) • Tokyo University of Marine Science and Technology (1) • Tokyo Gakugei University ( 2 ) • Tokyo Institute ofTechnology (4 ) • The University of Tokyo (12 ) • Tokyo Metropolitan University (1) • Tokyo University of Agriculture and Technology (1)• Hitotsubashi University (6 ) • Shizuoka University (1) • Japan Advanced Institute of Science and Technology (1) • Nagoya University (3 )• Toyohashi University of Technology ( 2 ) • Kyoto University ( 7 ) • Osaka City University (1) • Osaka University ( 2 ) • Nara Institute of
Science and Technology (1) • Okayama University ( 2 ) • Shimane University (3 ) • Kyushu University ( 2 ) • Oita University (1)21Graduate School Program

University Background of Students
Private universities• Kitasato University (1) • Keio University (8 ) • Shibaura Institute of Technology (1) • Chuo University (8 ) • Tokyo University of
Science (6 ) • Toyo University (1) • Japan Women’s University (1) • Nihon University (2 ) • Hosei University (7 ) • Waseda University (8 )• Nanzan University (1) • Kansai University (1) • Kyoto Sangyo University (1) • Ritsumeikan University (1) • Okayama University of
Science (1) • Kurume University (1)Foreign universities• Aston University (1) • University of California, Irvine (1) • California State University, Long Beach (1) • University of Campinas (1)• University of Colorado Boulder (2) • University of Dhaka (2) • University of Hawaii (1) • Jahangirnagar University (2) • Universityof Malaya (1) • Northeast Normal University (1) • Ohio University (2) • University of Rajshahi (2) • Stanford University (1) • Instituteof Applied Mathematics, AMSS, CAS (1) • University of Science and Technology of China (1) • Center for Analysis and Prediction,
China Seismological Bureau (1) • Northeastern University (1) • The Hong Kong University of Science and Technology (1)Degrees AwardedYear
Doctor of Philosophy

2011

2012

2013

4

6 [1]

6

2014

2015

2016

5

5

7

* [ ] Ph.D. on the basis of the dissertation only (included in the total)

AlumniNational and public universities, and public organizations• Obihiro University of Agriculture and Veterinary Medicine • University of Tsukuba • University of Hyogo • The University of Tokyo• The University of Electro-Communications • Saitama University • Nagoya University • Kyushu University • Kyushu Institute ofTechnology • University of the Ryukyus • The Institute of Statistical Mathematics • Tohoku University • Yokohama National University• Hokkaido University • Tokyo Institute of Technology • Hiroshima University • Oita University of Nursing and Health Sciences• JAXA’s Engineering Digital Innovation Center • Kyoto University • Nara Institute of Science and Technology • Bank of Japan• Japan Broadcasting Corporation • Railway Technical Research Institute • Statistical Information Institute for Consulting and Analysis• Government Pension Investment Fund • Public School
Private universities• Sapporo Gakuin University • Tokyo Health Care University • Meiji University • Doshisha University • Josai University • Nihon
University • Komazawa University • Aichi University of Technology • Tokyo University of Information Sciences • Shibaura Instituteof Technology • Rikkyo University • Waseda University
Foreign universities• Jahangirnagar University • Victoria University • Massey University • University of Otaga • Statistics New Zealand • University of
Rajshahi • University of California, Los Angeles • Asia-Pacific Center for Security Studies Department • Central South University• Hong Kong Baptist University • University of South Carolina • The University of Warwick
Private companies, etc.
• Hitachi, Ltd. Central Research Laboratory • NTT Communication Science Laboratories • Seiwa Kikaku • NLI Research Institute• Mizuho Trust and Banking • Nomura Securities Co., Ltd. • ATR Computational Neuroscience Laboratories • Toyota Motor
Corporation, Higashi-Fuji Technical Center • Schlumberger Limited • Macquarie Securities, Japan • Non-Life Insurance Rating
Organization of Japan • Barclays Global Investors • Open Technologies Corporation • Yamaha Corporation • Goldman Sachs
Asset Management L.P. • CLC bio Japan, Inc. • Bank of Tokyo-Mitsubishi UFJ • Pfizer Japan Inc. • Doctoral Institute for Evidence
Based Policy • Sony Corporation • NTTIT Corporation • Sompo Japan Insurance Inc. • Qualicaps Co.,Ltd. • Bridgestone
Corporation • Brain Pad Inc. • Sumitomo Chemical Co.,Ltd. • PricewaterhouseCoopers Aarata • Mitsubishi Tanabe Pharma
Corporation • Daiichi Sankyo Co.,Ltd. • Shizuoka Cancer Center • CPC Clinical Trial Hospital, Medipolis Medical Research Institute• CRD Association • Japan Society for the Promotion of Science • Tokyo Electric Power Company Holdings, Inc. • Asahi Kasei
Corporation • Honda R&D Co.,Ltd.

22

Facilities and Equipment

Computational Resources ( As of April 1, 2017 )Since July 2014, the ISM has maintained three different
supercomputer systems: the Supercomputer System for
Data Assimilation (nicknamed “A”), the Supercomputer
System for Statistical Science (nicknamed “I”), and theCommunal Cloud Computing System (nicknamed “C”).
Those are nicknamed in tribute to a former director general, the late Professor Hirotugu Akaike, who invented thefamous model selection criterion AIC. As of July 2015, thesystem I has just been expanded to have Haswell nodes.
System A is the world’s largest shared-memory supercomputer system and consists of two sets of SGI UV 2000
(each 256 nodes of 10-core Xeon E5-4650v2, 64 TB memory). Half of this system is taking part in the high-performance computing infrastructure (HPCI) project.
System I is a large distributed-memory supercomputer that consists of 400 nodes of SGI ICE X with Ivy Bridgeand 136 Haswell nodes, having 12,864 cores in total. The
system also includes a large-scale shared storage system(2.5 PB disk storage), physical random number generator boards, and a visualization system that supports a

The Supercomputer System for Data
Assimilation ( nicknamed A )maximum resolution of 4,096 × 2,160 and has a 200-inch
rear projection screen for 3D visualization.
System C consists of 64 Dell PowerEdge R620 (two10-core Xeon E5-2680v2, 256 GB memory). This systemprovides easy-to-use computing environments, such as
distributed-memory statistical computing environments
and Web servers, running on Apache CloudStack software.
In the main office, the primary local area network(LAN) consists of an Ethernet network using 10GBASESR for the main trunk and 1000Base-T for branches. The
personal computers in researchers' offices, and systems
A, I, and C are all connected to this network. A wireless
LAN system is also available in the immediate area of thebuilding occupied by the institute. These LAN systems
enable distributed processing and allow computing resources and statistical data to be used effectively. Comprehensive network security methods have been implemented, such as a firewall system, anti-virus software,and an intrusion prevention system. To encourage joint
research with researchers both in Japan and abroad, as
well as the exchange of e-mails, the network is connectedto the Internet through SINET5 (40 Gbps).

The Supercomputer System for Statistical
Science ( nicknamed I )The Communal Cloud Computing
System ( nicknamed C )Library and Materials ( As of April 1, 2017 )We have a large number of major Japanese/foreign
journals covering a wide variety of fields including statistics, mathematics, computer science and informatics. In
addition, we also have a large library consisting of books
on humanities, social science, biology, medical science,science and engineering.
Besides contributed to Japanese and foreign pub­lications, we also have a collection of journals that wepublish ourselves: Annals of the Institute of Sta­tistical
Mathematics (English; Springer), Proceedings of the Institute of Statistical Mathematics (Japanese), ISM Survey
Research Report (Statistical Researches mainly related
to the Japanese National Character), Computer Science
Monographs, Cooperative Research Re­ports (for collaborative research projects), Research Memorandum, ISM

Reports on Statistical Computing, and ISM Report on
Research and Education.
All materials are properly catalogued and can besearched from the web in order to meet the needs ofresearch­ers working in a wide of fields. We also accept
photocopy requests.
Japanese
books
19,15528%

Books
69,443

Japanese
periodicals
992
45%

Foreignperiodicals

Periodicals
2,223

1,231
55%

Foreign books
50,28872%

23

Finance and Buildings

Administration Subsidy and Others ( 2016 )Type

Personnel expenses

Non-personnel expenses

Total

729,430

864,105

1,593,535

Expenditure

Unit: ¥1,000

Accepted External Funds ( 2016 )Type

Joint research

Subcontracted research,
Trustee businessContract
researchers

Contribution for
scholarship

Total

Items

22

20

3

7

52

Income41,112

164,170

1,393

9,850

216,525
Unit: ¥1,000

Grant-in-Aid for Scientific Research “ KAKENHI” ( 2016 )Research Category

Items

Amount Granted

Grant-in-Aid for Scientific Research on Innovation Areas

2

11,570Grant-in-Aid for Scientific Research ( S )——Grant-in-Aid for Scientific Research ( A)3

23,010Grant-in-Aid for Scientific Research ( B)938,090Grant-in-Aid for Scientific Research ( C)

15

19,110Grant-in-Aid for Challenging Exploratory Research

4

4,810

Grant-in-Aid for Young Scientists ( B )

5

7,020

Grant-in-Aid for Research Activity Start-up

2

2,340

Grant-in-Aid for JSPS Fellows

3

4,030

43

109,980

Total

Unit: ¥1,000

Site and Buildings ( As of April 1, 2017 )24

Site Area

62,450m 2

Area for Buildings (total)

16,209m 2

Organization

Organization Diagram ( As of August 1, 2017 )■ Spatial and Time Series Modeling Group

Cooperative
Research
Committee

Department of Statistical Modeling■ Latent Structure Modeling Group■ Survey Science Group

Department of Data Science■ Metric Science Group■ Structure Exploration GroupCouncil

Scientific
Advisory
Board■ Complex System Modeling Group

Department of Mathematical
Analysis and Statistical Inference■ Mathematical Statistics Group■ Learning and Inference Group■ Computational Inference Group

Risk Analysis Research Center
Research and Development
Center for Data Assimilation

DirectorGeneral

Vice DirectorGeneral

Research Center for
Statistical Machine Learning
Data Science Center for Creative
Design and Manufacturing
School of Statistical Thinking

Center for Engineering andTechnical Support■ Computing Facilities Unit
■ Information Resources Unit
■ Media Development Unit

Library■ URA Station

Planning Unit

■ Gender Equality Subunit

Evaluation Unit
Administration Planningand Coordination Section

Information and Public Relations UnitIntellectual Property Unit
NOE Promotion Unit

National Institute ofPolar Research

Planning Section ( NIPR )NIPR / ISM
Joint Administration Office

The Institute of
Statistical Mathematics

Planning Section ( ISM )General Service Center25

Number of Staff ( As of April 1, 2017 )DirectorGeneral

Type

Director-General

Assistant Administ­ Technical
Professor Associate
Staff
Professor Professor rative Staff

Total

1

1

Department of Statistical Modeling

4

8

3

15

Department of Data Science

3

7

3

13

Department of Mathematical Analysis and Statistical Inference

9

3

3

15

School of Statistical Thinking

0Center for Engineering and Technical Support

1010

Administration Planning and Coordination Section

1

NIPR/ISM Joint Administration Office

15 ( 34 )

1 (1 )

16 ( 35)

16 ( 34 )

11 ( 1 )

71 ( 35)

Total

1

16

18
(

9

1

) Total number of staff of NIPR/ISM Joint Administration Office.

The number under Technical Staff at the Center for Engineering and Technical Support and Administrative Staff at the NIPR/ISM Joint Administration Officeinclude one each staff member who retired because of age but was reemployed in a different position.

Staff ( As of August 1, 2017 )Director-General

Tomoyuki HIGUCHI

Vice Director-General

Satoshi ITO

Vice Director-General

Satoshi YAMASHITA

Vice Director-General

Koji KANEFUJI

Department of Statistical Modeling
Director Junji NAKANO• Spatial and Time Series Modeling Group —————————————————————————————————————————————————————Prof.

Tomoyuki HIGUCHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Shinya NAKANOAssist. Prof.

Daisuke MURAKAMI

Assoc. Prof.

Genta UENO

• Complex System Modeling Group ——————————————————————————————————————————————————————————
Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Shinsuke KOYAMA

Assist. Prof.

Ayaka SAKATA

Assist. Prof.

Momoko HAYAMIZU

Visiting Prof.

Hiroshi MARUYAMA

Visiting Assoc. Prof. Yuji MIZUKAMI

Visiting Assoc. Prof. Shigeru SAITOU

Visiting Assoc. Prof. Tarou TAKAGUCHI• Latent Structure Modeling Group ——————————————————————————————————————————————————————————Prof.

Tomoko MATSUIProf.

Yoshinori KAWASAKI

Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Stephen WU

Assoc. Prof.

Ryo YOSHIDA

Department of Data Science
Director Yoshiyasu TAMURA• Survey Science Group —————————————————————————————————————————————————————————————————————

26

Prof.

Ryozo YOSHINO

Assoc. Prof.

Tadahiko MAEDA

Assist. Prof.

Masayo HIROSE

Project Assist. Prof. Yusuke INAGAKIAssist. Prof

Yoo Sung PARK

Project Assist. Prof. Kiyohisa SHIBAIOrganization

Staff
Department of Data Science
Visiting Prof.

Takatoshi IMADA

Visiting Prof.

Toru KIKKAWA

Visiting Prof.

Yoshimichi SATO

Visiting Prof.

Masato YONEDA

Visiting Prof.

Shintaro SONO

Kazufumi MANABE

Visiting Prof.

Fumi HAYASHI

Visiting Prof.

Masahiro MIZUTA

Saeko KIKUZAWA

Visiting Assoc. Prof. Takahito ABE

Visiting Assoc. Prof. Koken OZAKI

Visiting Assoc. Prof. Hiroko TSUNODA

Visiting Assoc. Prof. Taisuke FUJITA

Visiting Assoc. Prof. Wataru MATSUMOTO
Visiting Prof.
Visiting Prof.

Visiting Assoc. Prof. Tadayoshi FUSHIKI
Project Researcher

Naoko KATO

• Metric Science Group —————————————————————————————————————————————————————————————————
Prof.

Yoshiyasu TAMURA

Prof.

Satoshi YAMASHITA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Hisashi NOMA

Assist. Prof.

Nobuo SHIMIZU

Visiting Prof.

Michiko WATANABE

Visiting Prof.

Kenichi MIURA

Visiting Prof.

Hiroko NAKANISHI

Visiting Prof.

Makoto SHIMIZU

Visiting Assoc. Prof. Fumitake SAKAORI

Visiting Assoc. Prof. Michihiro OTA

Visiting Assoc. Prof. Kazuhiro HIDE

Project Researcher

Ai KAWAMORI• Structure Exploration Group —————————————————————————————————————————————————————————————
Prof.

Koji KANEFUJI

Assoc. Prof.

Naomasa MARUYAMA

Assist. Prof.

Shunichi NOMURA

Project Researcher

Hiroka HAMADA

Assoc. Prof.

Jun ADACHI

Department of Mathematical Analysis and Statistical Inference
Director Satoshi KURIKI• Mathematical Statistics Group ———————————————————————————————————————————————————————————————
Prof.

Satoshi KURIKI

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Shogo KATO

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Visiting Prof.

Akimichi TAKEMURA• Learning and Inference Group —————————————————————————————————————————————————————————————
Prof.

Shinto EGUCHIProf.

Kenji FUKUMIZU

Prof.

Shiro IKEDA

Assoc. Prof.

Daichi MOCHIHASHI

Prof.

Hironori FUJISAWA• Computational Inference Group ———————————————————————————————————————————————————————————
Prof.

Yoshihiko MIYASATO

Prof.

Atsushi YOSHIMOTO

Prof.

Satoshi ITO

Prof.

Akiko TAKEDA

Assist. Prof.

Mirai TANAKA

Visiting Prof.

Eitarou AIYOSHI

Risk Analysis Research Center
Director Satoshi YAMASHITA

Vice Director Shogo KATO

Satoshi YAMASHITA

Prof.

Satoshi KURIKI

Prof.

Shinto EGUCHIProf.

Koji KANEFUJI

Prof.

Atsushi YOSHIMOTO

Prof.

Yoshinori KAWASAKIProf.

Yoshinori KAWASAKI

Assoc. Prof.

Masayuki HENMI

Assoc. Prof.

Fumikazu MIWAKEICHI

Assoc. Prof.

Jiancang ZHUANG

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Ikuko FUNATOGAWA

Assoc. Prof.

Shogo KATO

Assoc. Prof.

Yumi TAKIZAWA

Assoc. Prof.

Hisashi NOMA

Assoc. Prof.

Shuhei MANO

Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Takaaki SHIMURA

Assist. Prof.

Teppei OGIHARA

Assist. Prof.

Shunichi NOMURA

Project Assist. Prof. Takahiro OTANIProf.

Project Assist. Prof. Shizu ITAKA

Project Assist. Prof. Hayafumi WATANABE

Project Assist. Prof. Yuta TANOUE

Project Assist. Prof. Takao KUMAZAWA

Project Assist. Prof. Junchao ZHANG

Visiting Prof.

Rinya TAKAHASHI

27

Risk Analysis Research Center
Visiting Prof.

Yo SHIINA

Visiting Prof.

Manabu IWASAKI

Visiting Prof.

Toshiya SATO

Visiting Prof.

Satoshi TERAMUKAI

Visiting Prof.

Tatsuhiko TSUNODA

Visiting Prof.

Naoki SAKAI

Visiting Prof.

Mihoko MINAMI

Visiting Prof.

Satoshi TAKIZAWA

Visiting Prof.

Toshihiro HORIGUCHI

Visiting Prof.

Shunji HASHIMOTO

Visiting Prof.

Yasuhiro KUBOTA

Visiting Prof.

Naoto KUNITOMO

Visiting Prof.

Toshio HONDA

Visiting Prof.

Hideatsu TSUKAHARA

Visiting Prof.

Hiroshi TSUDA

Visiting Prof.

Sadaaki MIYAMOTO

Visiting Prof.

Michiko MIYAMOTO

Visiting Prof.

Tadashi ONO

Visiting Prof.

Yoichi NISHIYAMA

Visiting Prof.

Satoshi FUJII

Visiting Prof.

Takaaki YOSHINO

Visiting Prof.

Masakazu ANDO

Visiting Prof.

Shusaku TSUMOTO

Visiting Prof.

Shigeyuki MATSUIVisiting Prof.

Toshinao YOSHIBA

Visiting Prof.

Nakahiro YOSHIDA

Visiting Prof.

Masaaki FUKASAWA

Visiting Prof.

Yasutaka SHIMIZU

Visiting Assoc. Prof. Hisayuki HARA

Visiting Assoc. Prof. Takafumi KUBOTA

Visiting Assoc. Prof. Hisateru TACHIMORI

Visiting Assoc. Prof. Makoto TOMITA

Visiting Assoc. Prof. Masakazu FURUKAWA

Visiting Assoc. Prof. Ryota NAKAMURA

Visiting Assoc. Prof. Kazushi MARUO

Visiting Assoc. Prof. Atsushi GOTO

Visiting Assoc. Prof. Masataka TAGURI

Visiting Assoc. Prof. Takashi KAMEYA

Visiting Assoc. Prof. Kenichi KAMO

Visiting Assoc. Prof. Masashi KONOSHIMA

Visiting Assoc. Prof. Tetsuji TONDA

Visiting Assoc. Prof. Takaki IWATA

Visiting Assoc. Prof. Bogdan Dumitru ENESCU

Visiting Assoc. Prof. Hitoshi MOTOYAMA

Visiting Assoc. Prof. Yukihiko OKADA

Visiting Assoc. Prof. Toshihiko KAWAMURA

Visiting Assoc. Prof. Seisho SATO

Project Researcher

Project Researcher

Shonosuke SUGASAWA

Hideaki NAGAHATA

Research and Development Center for Data Assimilation
Director Genta UENO

Vice Director Shinya NAKANOProf.

Tomoyuki HIGUCHIProf.

Yoshiyasu TAMURA

Prof.

Junji NAKANOProf.

Yukito IBA

Assoc. Prof.

Genta UENO

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Shinya NAKANOAssist. Prof.

Stephen WU

Assist. Prof.

Shunichi NOMURA

Visiting Prof.

Takashi WASHIO

Visiting Prof.

Nobuhiko TERUIProject Assoc. Prof. Masaya SAITO

Project Assist. Prof. Yuya ARIYOSHI

Visiting Prof.

Shinichi OTANIVisiting Prof.

Visiting Prof.

Tadahiko SATO

Yoichi MOTOMURA

Visiting Assoc. Prof. Hiroshi FUJISAKI

Visiting Assoc. Prof. Kazuyuki NAKAMURA

Visiting Assoc. Prof. Hiromichi NAGAO

Visiting Assoc. Prof. Hiroshi KATO

Visiting Assoc. Prof. Eiji MOTOHASHI

Visiting Assoc. Prof. Tsukasa ISHIGAKI

Visiting Assoc. Prof. Yosuke FUJII

Research Center for Statistical Machine Learning
Director Kenji FUKUMIZU
Prof.

Kenji FUKUMIZU

Prof.

Tomoko MATSUIProf.

Shinto EGUCHIProf.

Yoshihiko MIYASATO

Prof

Satoshi ITOProfShiro IKEDA

Prof

Satoshi KURIKI

Prof

Akiko TAKEDAProf

Hironori FUJISAWA

Assoc. Prof.

Daichi MOCHIHASHI

Assoc. Prof.

Shinsuke KOYAMA

Assoc. Prof.

Kazuhiro MINAMI

Assist. Prof.

Mirai TANAKA

Project Assist. Prof. Song LIU

Project Assist. Prof. Mikio MORII

Project Assist. Prof. Matthew Christopher AMES

Visiting Prof.

Katsuki FUJISAWA

Visiting Prof.

Yoshiki YAMAGATA

Project Assist. Prof. Motonobu KANAGAWA
Visiting Prof.

Takashi TSUCHIYA

Visiting Prof.

Visiting Prof.

Hiroshi KURATA

Visiting Assoc. Prof. Yuji SHINANO

Visiting Assoc. Prof. Hiroshi SOMEYAVisiting Assoc. Prof. Makoto YAMADA

Project Researcher

Visiting Assoc. Prof. Arthur GRETTON

28

Vice Director Tomoko MATSUIMasataka GOTO

Jin ZHOU

Organization

Staff
Data Science Center for Creative Design and Manufacturing
Director Ryo YOSHIDA

Vice Director Hironori FUJISAWA

Prof.

Hironori FUJISAWA

Prof.

Kenji FUKUMIZU

Prof.

Akiko TAKEDA

Assoc. Prof.

Ryo YOSHIDA

Assoc. Prof.

Shinya NAKANOAssoc. Prof.

Daichi MOCHIHASHI

Assist. Prof.

Stephen WU

Project Assist. Prof. Guillaume LAMBARD

Project Researcher

Hironao YAMADA

Visiting Assoc. Prof. Hiroshi YAMASHITA

School of Statistical Thinking
Director Yoshinori KAWASAKI

Vice Director Genta UENOProf.

Satoshi ITO

Prof.

Yukito IBA

Prof.

Kenji FUKUMIZU

Assoc. Prof.

Naomasa MARUYAMA

Assoc. Prof.

Kenichiro SHIMATANI

Assoc. Prof.

Masayuki HENMI

Adjunct Prof.

Kunio SHIMIZU

Adjunct Prof.

Nobuhisa KASHIWAGI

Project Assoc. Prof. Naoki KAMIYA

Project Assist. Prof. Syogo MIZUTAKA

Visiting Assoc. Prof. Osamu KOMORI

Visiting Assoc. Prof. Kei TAKAHASHI

Center for Engineering and Technical Support
Director

Yoshinori KAWASAKI

Deputy Manager

Yuriko WATANABE

Head of Computing Facilities Unit

Kazuhiro NAKAMURA

Head of Media Development Unit

Akiko NAGASHIMA

Vice Director
Senior Specialist

Jun ADACHI
Saeko TANAKA

Head of Information Resources Unit

Saeko TANAKA

Library
Head Yoshinori KAWASAKI

Administration Planning and Coordination Section
Director Tomoyuki HIGUCHIHead of Planning Unit

Satoshi ITOHead of Evaluation Unit

Koji KANEFUJI

Head of Information and Public Relations Unit

Koji KANEFUJI

Head of Intellectual Property Unit

Satoshi YAMASHITA

Head of NOE Promotion Unit

Satoshi ITO• URA Station ————————————————————————————————————————————————————————————————————————
Leader

Kozo KITAMURA

SubleaderMotoi OKAMOTO

Research Administrator

Keisuke HONDA

Research Administrator

Yoko OGAWA• Gender Equality Subunit ————————————————————————————————————————————————————————————————
Head of Gender Equality Subunit

Satoshi ITO

29

Staff
NIPR/ISM Joint Administration OfficeDirector of NIPR/ISM Joint Administration OfficeKazuhiko HASEGAWA

Director of General Service Center

Atushi MATSUO• Planning Section ( ISM ) —————————————————————————————————————————————————————————————————————
Head of Planning Section Tsuyoshi ABE
Deputy Head

Masatoshi NAKAMURATeam Leader

Junko MATSUYAMATeam Leader

Koji MORITATeam Leader

Ichiro KAWAJI• Planning Section ( NIPR) ————————————————————————————————————————————————————————————————————
Head of Planning Section Akiyoshi KOJIMA
Deputy Head

Motokazu TOYODATeam Leader

Michihito SAKURAITeam Leader

Masaki ONIZAWATeam Leader

Hitoshi HIRAYAMA• General Service Center —————————————————————————————————————————————————————————————————————
Vice Director of General Service Center Tatsuya NAKANO
Deputy Head

Koji SAKAMOTO

Deputy Head

Hiroki TERAUCHI

Deputy Head

Masayuki KOBAYASHITeam Leader

Hiromi KATOTeam Leader

Hiroaki ARAI

Team Leader

Kentaro TSUJIITeam Leader

Takeshi KUWAHARATeam Leader

Takuya SAITO

Specialist

Yoshihiro YAMADASpecialist

Yoriko HAYAKAWA

Council of The Institute of Statistical Mathematics ( As of April 1, 2017 )30

Yasushi AKIYAMA

Professor, Graduate School of Information Science and Engineering, Tokyo Institute of Technology

Masahiro MIZUTA

Professor, Information Initiative Center, and Graduate School of Information Science and Technology, Hokkaido University

Shigeru OBAYASHI
Nakahiro YOSHIDA

Director Institute of Fluid Science Tohoku University

Masayuki UCHIDA

Professor, Graduate School of Engineering Science, Osaka University

Ryuei NISHII

Professor, Institute of Mathematics for Industry, Kyushu University

Yasuhiro OMORI

Professor, Faculty of Economics, University of Tokyo

Shoichi YOKOYAMA

Professor, Language Change Division, National Institute for Japanese Language and Linguistics

Masato OKADA

Professor, Graduate School of Frontier Sciences, The University of Tokyo

Michiko WATANABE

Professor, The Graduate School of Health Management, Keio University

Ryozo YOSHINO

Professor, Research Organization of Information and Systems

Satoshi ITOProfessor ( Vice Director-General, ISM )Satoshi YAMASITA

Professor ( Vice Director-General, ISM )Kouji KANEFUJI

Professor ( Vice Director-General, ISM )Junji NAKANOProfessor (Director of Department of Statistical Modeling, ISM )Yoshiyasu TAMURA

Professor (Director of Department of Data Science, ISM )Satoshi KURIKI

Professor (Director of Department of Mathematical Analysis and Statistical Inference, ISM )Professor, Graduate School of Mathematical Sciences, The University of Tokyo

Yshinori KAWASAKI

Professor (Director of Center for Engineering and Technical Support, ISM )Tomoko MATSUI

Professor (Department of Statistical Modeling, ISM )Kenji FUKUMIZU

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Yoshihiko MIYASATO

Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Organization

Cooperative Research Committee ( As of June 1, 2017 )Tadahiko SATO

Professor, Faculty of Business Sciences, University of Tsukuba

Kunihiko TAKAHASHI
Akinobu TAKEUCHI

Associate Professor, Department of Biostatistics, Nagoya University Graduate School of Medicine

Takahiro TSUCHIYA

Professor, Center for Data Science, Yokohama City University

Hiroyuki MINAMI

Professor, Information Initiative Center, Hokkaido University

Junji NAKANOProfessor ( Director of Department of Statistical Modeling, ISM )Koji KANEFUJI

Professor ( Director of Department of Data Science, ISM )Shiro IKEDA

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Shinto EGUCHI

Professor ( Department of Mathematical Analysis and Statistical Inference, ISM )Professor, Faculty of Humanities and Social Sciences, Jissen Women's University

Managing Committee of School of Statistical Thinking ( As of April 1, 2017 )Satoshi IMURA

Professor (Bioscience Group, National Institute of Polar Research )Yoshimichi OCHI

Trustee, Vice-president (National University Corporation OITA UNIVERSITY )Takafumi KUSANO

Representative Director / Co-Founder (BrainPad Inc.)

Wataru SAKAMOTO

Professor (Graduate School of Environmental and Life Science, OKAYAMA UNIV. )Yo SHEENA

Professor (Academic Assembly School of Humanities and Social Sciences Institute of Social Sciences, Shinshu University )Yoshinori KAWASAKI

Director (School of Statistical Thinking, ISM )Genta UENO

Vice Director (School of Statistical Thinking, ISM )Yukito IBA

Professor (Department of Statistical Modeling, ISM )Kenichiro SHIMATANI

Associate Professor (Department of Data Science, ISM )Satoshi ITOProfessor ( ViceDirector-General, ISM )Research Ethics Review Committee ( As of April 1, 2017 )Specialist on epidemiology
and social research

Kazuo SEIYAMA

Professor Emeritus, Tokyo University / Deputy Director, Research Center for
Science Systems of Japan Society for the Promotion of Science

Specialist on epidemiology
and social research

Keiko SATO

Associate professor, Kyoto University Hospital, Institute for Advancement of Clinicaland Translational Science Department of EBM ResearchSpecialist in the field of ethics and law

Hitomi NAKAYAMA

Lawyer, Kasumigaseki-Sogo Law Offices

Person in citizen’s position

Yutaka KURIKI

Kindergarten Director and Elementary School Director, KEIMEI GAKUEN

Research education staff of ISM

Tadahiko MAEDA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Yoo Sung PARK

Assist Professor ( Department of Data Science, ISM )Research education staff of ISM

Koji KANEFUJI

Professor ( Department of Data Science, ISM )Research education staff of ISM

Ikuko FUNATOGAWA

Associate Professor ( Department of Data Science, ISM )Research education staff of ISM

Shuhei MANO

Associate Professor (Department of Mathematical Analysis and Statistical Inference, ISM )Professor Emeritus ( As of April 1, 2017 )Kameo MATUSITA
Ryoichi SHIMIZU
Tadashi MATSUNAWA
Yoshiaki ITOH
Makio ISHIGURO
Nobuhisa KASHIWAGI

Sigeki NISIHIRA
Noboru OHSUMI
Masami HASEGAWA
Yasumasa BABA
Yosihiko OGATA
Takashi NAKAMURA

Tatsuzo SUZUKI
Masakatsu MURAKAMI
Yoshiyuki SAKAMOTO
Katsuomi HIRANO
Hiroe TSUBAKI

Giitiro SUZUKI
Kunio TANABE
Takemi YANAGIMOTO
Masaharu TANEMURAGenshiro KITAGAWA

31

History

1944

June

Based on a proposal submitted at an academic study conference in December 1943, the organization was
founded as an institute under the direct control of the Ministry of Education. This proposal aimed to provide
supervision for studies looking into the mathematical principles of probability and their application, and was
also intended to facilitate, unify and promote the publication of research results.

1947

April

The affiliated statistical specialists’ school was opened.

May

The Institute was divided into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. (statisticaltheories for the natural sciences), and the 3rd Research Dept. ( statistical theories for the social sciences ).

1949

June

The Institute was placed under the control of the Ministry of Education because of the enforcement of the Ministryof Education Establishment Law.

1955

September

Reorganized into the 1st Research Dept. ( fundamental theories ), the 2nd Research Dept. ( natural and social science
theories ), and the 3rd Research Dept. ( operations, research, statistical analysis theories ). The laboratory
system, comprising 9 laboratories and the research guidance promotion room, was adopted.

1969

October

1971

April

The 4th Research Dept. (informatics theories) was instituted.

1973

April

The 5th Research Dept. (prediction and control theories) was instituted.
The 6th Research Dept. (statistical theories of human behavior) was instituted.

1975

October

1979

November

1985

April

Repositioned as a National Inter-University Research Institute due to the regulation change. The new mission
includes providing facilities and skills to other universities, in addition to conducting cutting-edge research on
statistical mathematics. Accordingly, the institute was reorganized into four basic research departments (Fundamental Statistical Theory, Statistical Methodology, Prediction & Control, and Interdisciplinary Statistics ) andtwo strategic centers ( Statistical Data Analysis Center and Statistical Education & Information Center ). The
Statistical Technical Training Center was terminated.

1988

October

The Dept. of Statistical Science was instituted in the School of Mathematical and Physical Science, part of the
Graduate University for Advanced Studies ( SOKENDAI ).

1989

June

The Institute was reorganized as an Inter-University Research Institute based on the National School Establishment Law.

1993

April

The Planning Coordination Chief System was instituted.

1997

April

The affiliated Statistical Data Analysis Center was reorganized into the Center for Development of StatisticalComputing, and the Statistical Education and Information Center was reorganized into the Center for Information
on Statistical Sciences.

2003

September

2004

April

The Institute was reorganized into the Institute of Statistical Mathematics, part of the Research Organi­zation of Information and Systems of the Inter-University Research Institute based on the National University Corporation Law.
The Planning Coordination Chief System was abolished and the position of Vice Director-General was instituted instead. The Dept. of Statistical Science in the School of Mathe­matical and Physical Science, SOKENDAI, was reorganized. In addition, the Dept. of Statistical Science and the School of Multidisciplinary Sciences were instituted.

2005

April

The research organization was reorganized into three research departments ( the Department of Statis­tical
Modeling, the Department of Data Science, and the Department of Mathematical Analysis and Statistical Inference ). The affiliated Center for Development of Statistical Computing, the Center for Information on Statistical
Sciences, and the Engineering and Technical Services Section were inte­grated into the Center for Engineeringand Technical Support. The Risk Analysis Research Center was instituted.

2006

April

The Administration Planning Coordination Unit was instituted.

2008

April

The Research Innovation Center was instituted.
The Administration Planning and Coordination Unit was reorganized into the Administration Planning and Coordination Section (hereafter APCS), within which the Intellectual Property Unit, the Evaluation Unit and the Information and Public Relations Unit were instituted.

2009

January

The Planning Unit was instituted within APCS.

October

The Institute was moved to 10-3 Midori-cho, Tachikawa, Tokyo.

2010

The Information Research Building was constructed.

The Prediction and Knowledge Discovery Research Center was instituted.

June

Officially opened the Akaike Guest House.

July

Reorganized the Administration Office to create the NIPR/ISM Joint Administration Office and launch the General
Service Center. The NOE Forwarding Unit ( now we call “NOE Promotion Unit” ) was instituted within APCS.

2011

January

Research and Development Center for Data Assimilation was instituted.
Survey Science Center was instituted.

2012

January

Research Center for Statistical Machine Learning, Service Science Research Center and School of StatisticalThinking were instituted.

2014

July
December

2017

32

A new office building was constructed in Minato Ward.

July

The URA Station was instituted within the Planning Unit.
The Office of Female Researcher Development was instituted within the Planning Unit.
Data Science Center for Creative Design and Manufacturing was instituted.

The Institute of
Statistical Mathematics(

Bus stop
TachikawaShiyakusho

to Kamikitadai

Tachikawa City Hall
Bus stop ( Tachikawa Academic Plaza))

Bus stop (Saibansho-mae)
Courthouse

Takamatsu

Local Autonomy
College

National Institutefor Japanese Language
and Linguistics

Tachikawa
Police Station
Disaster
MedicalCenterTama
MonorailShowa Kinen Park

Ome Line

Tachikawa-kita

Tachikawa
Access to the ISM

Chuo Lineto Shinjuku,
Tokyo →

Tama Monorail—10 min walk from Takamatsu Sta.
Tachikawa Bus— Tachikawa Academic Plaza bus stop
— 5 min walk from Saibansho-mae
or Tachikawa-Shiyakusho bus stop

10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
Tel: +81- ( 0 ) 50-5533-8500 Fax: +81- ( 0 ) 42-527-9302

http://www.ism.ac.jp/